The present invention relates to registration of medical images, and more particularly, to automatic semantics driven registration of medical images of a patient.
Image registration is a crucial technique to provide comparisons of medical images of a patient. For example, image registration can be used to compare medical images of a tumor before and after some treatment is administered or for pre and post interventional (e.g., stent placements) medical image comparisons.
Some state of the art workstations provide tools and algorithms for rigid image alignment (i.e., translation and rotation). However, due to the elastic nature of the human body, the limited degrees of freedom may not be sufficient to ensure that corresponding anatomical structures in different medical images are well-aligned to each other. A variety of elastic image registration techniques have recently been proposed. In such techniques, a number of image similarity measures are typically used together with various optimization algorithms to attempt to ensure that corresponding structures in the medical images are matched to each other. Typically, these approaches utilize a regularization term that imposes some smoothness on the deformation field to make the problem less ill-posed. The resulting deformation field is therefore a compromise between attention to detail and numerical stability and divergence. One shortcoming of such global regularization is that regions of specific interest in the medical images are not treated differently from other areas that are not of interest. In addition, changes of the image data, such as changes due to interventions (e.g., stent placement) or partial organ resections, are typically not handles well by such conventional image registration techniques.